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一种基于图注意力网络的异质信息网络表示学习框架

康世泽 吉立新 张建朋

康世泽, 吉立新, 张建朋. 一种基于图注意力网络的异质信息网络表示学习框架[J]. 电子与信息学报, 2021, 43(4): 915-922. doi: 10.11999/JEIT200034
引用本文: 康世泽, 吉立新, 张建朋. 一种基于图注意力网络的异质信息网络表示学习框架[J]. 电子与信息学报, 2021, 43(4): 915-922. doi: 10.11999/JEIT200034
Shize KANG, Lixin JI, Jianpeng ZHANG. Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network[J]. Journal of Electronics and Information Technology, 2021, 43(4): 915-922. doi: 10.11999/JEIT200034
Citation: Shize KANG, Lixin JI, Jianpeng ZHANG. Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network[J]. Journal of Electronics and Information Technology, 2021, 43(4): 915-922. doi: 10.11999/JEIT200034

一种基于图注意力网络的异质信息网络表示学习框架

doi: 10.11999/JEIT200034
基金项目: 国家自然科学基金(61521003)
详细信息
    作者简介:

    康世泽:男,1991年生,博士生,研究方向为知识图谱

    吉立新:男,1969年生,研究员,博士生导师,研究方向为电信网关信息防护、大数据分析

    张建朋:男,1988年生,助理研究员,研究方向为大数据分析

    通讯作者:

    康世泽 xiaozebixia@163.com

  • 中图分类号: TN919.2

Heterogeneous Information Network Representation Learning Framework Based on Graph Attention Network

Funds: The National Natural Science Foundation of China (61521003)
  • 摘要: 常用的异质信息网络有知识图谱和具有简单模式层的异质信息网络,它们的表示学习通常遵循不同的方法。该文总结了知识图谱和具有简单模式层的异质信息网络之间的异同,提出了一个通用的异质信息网络表示学习框架。该文提出的框架可以分为3个部分:基础向量模型,基于图注意力网络的传播模型以及任务模型。基础向量模型用于学习基础的网络向量;传播模型通过堆叠注意力层学习网络的高阶邻居特征;可更换的任务模型适用于不同的应用场景。与基准模型相比,该文所提框架在知识图谱的链接预测任务和异质信息网络的节点分类任务中都取得了相对不错的效果。
  • 图  1  知识图谱传播模型示意图

    图  2  Conv-TransE示意图

    表  1  简单模式层异质信息网络数据集的统计信息

    数据集节点#节点关系#连边#训练#验证#测试
    DBLPPaper(P)13769AP
    PC
    PT
    30632
    13769
    86739
    24004001200
    Author(A)13941
    Conference(C)20
    Term(T)8623
    IMDBMovie(M)5473MA
    MD
    15814
    5473
    1800300900
    Actor(A)6725
    Director(D)2761
    下载: 导出CSV

    表  2  简单模式层异质信息网络的节点分类性能

    数据集指标DeepWalkEsimMetapath2vecHANHE-GAN-NCVariant1Variant2
    DBLPMacro-F183.1592.4791.6392.5294.3192.2694.16
    Micro-F185.7793.6092.6493.6795.1793.4294.81
    IMDBMacro-F148.3433.8945.1352.2953.5850.3553.11
    Micro-F152.4835.2549.3855.8657.9253.9657.32
    下载: 导出CSV

    表  3  知识图谱的链接预测任务性能

    数据集FB15k-237WN18RR
    指标MRRHits@1Hits@3Hits@10MRRHits@1Hits@3Hits@10
    TransE0.2790.190.380.440.2420.040.440.53
    ConvE0.3150.240.350.490.4610.420.470.53
    ConvKB0.2850.190.320.470.2630.060.450.55
    SACN0.3520.260.390.540.4630.430.480.54
    relationPrediction0.5180.460.540.630.4400.360.480.58
    HE-GAN-LP0.5230.460.560.660.4680.410.500.59
    Variant30.5200.450.550.640.4470.370.480.58
    下载: 导出CSV
  • SHI Chuan, LI Yitong, ZHANG Jiawei, et al. A survey of heterogeneous information network analysis[J]. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(1): 17–37. doi: 10.1109/TKDE.2016.2598561
    于洪涛, 丁悦航, 刘树新, 等. 一种基于超节点理论的本体关系消冗算法[J]. 电子与信息学报, 2019, 41(7): 1633–1640. doi: 10.11999/JEIT180793

    YU Hongtao, DING Yuehang, LIU Shuxin, et al. Eliminating structural redundancy based on super-node theory[J]. Journal of Electronics &Information Technology, 2019, 41(7): 1633–1640. doi: 10.11999/JEIT180793
    BORDES A, USUNIER N, GARCIA-DURÁN A, et al. Translating embeddings for modeling multi-relational data[C]. The 26th International Conference on Neural Information Processing Systems, Lake Tahoe, USA, 2013: 2787–2795.
    DETTMERS T, MINERVINI P, STENETORP P, et al. Convolutional 2D knowledge graph embeddings[C]. The 32nd AAAI Conference on Artificial Intelligence (AAAI-18), New Orleans, USA, 2018: 1811–1818.
    NATHANI D, CHAUHAN J, SHARMA C, et al. Learning attention-based embeddings for relation prediction in knowledge graphs[C]. The 57th Annual Meeting of the Association for Computational Linguistics, Florence, Italy, 2019: 4710–4723. doi: 10.18653/v1/P19-1466.
    DONG Yuxiao, CHAWLA N V, SWAMI A, et al. Metapath2vec: Scalable representation learning for heterogeneous networks[C]. The 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 2017: 135–144. doi: 10.1145/3097983.3098036.
    WANG Xiao, JI Houye, SHI Chuan, et al. Heterogeneous graph attention network[C]. The World Wide Web Conference, San Francisco, USA, 2019: 2022–2032. doi: 10.1145/3308558.3313562.
    MIKOLOV T, CHEN Kai, CORRADO G, et al. Efficient estimation of word representations in vector space[C]. The 1st International Conference on Learning Representations, Scottsdale, Arizona, 2013: 1–12.
    SHANG Chao, TANG Yun, HUANG Jing, et al. End-to-end structure-aware convolutional networks for knowledge base completion[C]. The AAAI Conference on Artificial Intelligence, Hawaii, USA, 2019: 3060–3067. doi: 10.1609/aaai.v33i01.33013060.
    VELIČKOVIĆ P, CUCURULL G, CASANOVA A, et al. Graph attention networks[C]. International Conference On Learning Representations, Vancouver, Canada, 2018: 1–12.
    NGUYEN D Q, NGUYEN T D, NGUYEN D Q, et al. A novel embedding model for knowledge base completion based on convolutional neural network[C]. 2018 Conference of the North American Chapter of the Association for Computational Linguistics, New Orleans, USA, 2018: 327–333. doi: 10.18653/v1/N18-2053.
    PEROZZI B, AL-RFOU R, SKIENA S, et al. DeepWalk: Online learning of social representations[C]. The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, USA, 2014: 701–710. doi: 10.1145/2623330.2623732.
    SHANG Jingbo, QU Meng, LIU Jialu, et al. Meta-path guided embedding for similarity search in large-scale heterogeneous information networks[J]. arXiv preprint arXiv: 1610.09769v1, 2016.
    KINGMA D P and BA J. Adam: A method for stochastic optimization[C]. The 3rd International Conference for Learning Representations, San Diego, USA, 2015: 1–15.
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  • 文章访问数:  195
  • HTML全文浏览量:  50
  • PDF下载量:  39
  • 被引次数: 0
出版历程
  • 收稿日期:  2020-01-09
  • 修回日期:  2020-12-06
  • 网络出版日期:  2020-12-15
  • 刊出日期:  2021-04-20

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